ResNet architecture follows a framework called deep residual learning that is designed to solve the problem of degradation. To turn a plain network into a residual network, shortcut connections are inserted between groups of layers [1]}.
For this experiment, the model is reduced to a single block ResNet architecture. Final model contains total 87,170 parameters with 85,762 trainable parameters.
